In which scenarios is a cognitive discrepancy analysis appropriate for LD identification, and what are the limitations of discrepancy models?

• A. Discrepancy analysis is appropriate when there is a significant IQ-achievement difference; limitations include over-identification with high-IQ students, under-identification with certain populations, questionable validity; RTI is often preferred.
• B. Used in all cases regardless of IQ-achievement difference.
• C. Discrepancy analysis has no limitations.
• D. RTI replaces all testing.

Answer: (C)

Cognitive discrepancy analysis rests on the idea that a learning disability shows up as an unexpected gap between what a student can do conceptually (as measured by IQ) and what they actually show in academic achievement. So, it’s most appropriate when there is a sizable IQ–achievement difference on standardized tests, suggesting the student’s achievement is below what would be expected from their cognitive potential.

But there are important limitations. Measurement error in both IQ and achievement tests can create artificial gaps, or mask real difficulties. Many students with LD may not exhibit a large discrepancy at all, so they could be missed if we rely solely on this method. The approach can also over-identify students with high IQs (labeling them as LD despite adequate overall functioning) and under-identify students from certain populations where tests are biased or where instructional opportunities differ. Additionally, the discrepancy is a static snapshot and doesn’t capture how a student responds to instruction over time. Because of these concerns, many practitioners use RTI data and a broader set of information alongside or instead of discrepancy analysis.